diff --git a/cal_flops.py b/cal_flops.py
new file mode 100644
index 0000000..b9d25eb
--- /dev/null
+++ b/cal_flops.py
@@ -0,0 +1,101 @@
+import os
+import argparse
+import json
+import time
+import torch
+import torch.nn as nn
+from torch.optim.lr_scheduler import ReduceLROnPlateau
+from torch.utils.data import DataLoader
+import pytorch_lightning as pl
+from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
+from look2hear.utils.parser_utils import prepare_parser_from_dict, parse_args_as_dict
+import look2hear.models
+import look2hear.videomodels
+import yaml
+from ptflops import get_model_complexity_info
+from rich import print
+
+def check_parameters(net):
+    """
+        Returns module parameters. Mb
+    """
+    parameters = sum(param.numel() for param in net.parameters())
+    return parameters / 10 ** 6
+
+
+def flops_selective_scan_fn(B=1, L=256, D=768, N=16, with_D=True, with_Z=False, with_Group=True, with_complex=False):
+    """
+    u: r(B D L)
+    delta: r(B D L)
+    A: r(D N)
+    B: r(B N L)
+    C: r(B N L)
+    D: r(D)
+    z: r(B D L)
+    delta_bias: r(D), fp32
+    
+    ignores:
+        [.float(), +, .softplus, .shape, new_zeros, repeat, stack, to(dtype), silu] 
+    """
+    assert not with_complex 
+    # https://github.com/state-spaces/mamba/issues/110
+    flops = 9 * B * L * D * N
+    if with_D:
+        flops += B * D * L
+    if with_Z:
+        flops += B * D * L    
+    return flops
+
+def selective_scan_flop_jit():
+    B, D, L = 126, 512, 250
+    N = 16
+    flops = flops_selective_scan_fn(B=B, L=L, D=D, N=N, with_D=True, with_Z=True, with_Group=True)
+    return flops
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--exp_dir", default="exp/tmp", help="Full path to save best validation model"
+)
+
+with open("configs/tfgnet.yml") as f:
+    def_conf = yaml.safe_load(f)
+parser = prepare_parser_from_dict(def_conf, parser=parser)
+
+arg_dic, plain_args = parse_args_as_dict(parser, return_plain_args=True)
+audiomodel = getattr(look2hear.models, arg_dic["audionet"]["audionet_name"])(
+    sample_rate=arg_dic["datamodule"]["data_config"]["sample_rate"],
+    **arg_dic["audionet"]["audionet_config"]
+)
+
+with torch.cuda.device(3):
+    a = torch.randn(1, 1, 16000).cuda()
+    total_macs = 0
+    total_params = 0
+    # DPRNN
+    model = audiomodel.cuda()
+    with torch.no_grad():
+        macs, params = get_model_complexity_info(
+            model, (16000,), as_strings=False, print_per_layer_stat=True, verbose=False
+        )
+    # print(model(a).shape)
+    total_macs += macs
+
+    # selective_scan
+    macs = selective_scan_flop_jit() * 24 
+    
+    total_macs += macs
+
+    # in_proj
+    macs = 24 * 120 * 10 ** 6 
+    
+    total_macs += macs
+
+    # dt_proj
+    macs = 24 * 4.12 * 10 ** 9
+    
+    total_macs += macs
+    total_params += params
+    print("MACs: ", total_macs / 10.0 ** 9)
+    print("Params: ", total_params / 10.0 ** 6)
+    # for i in range(1000):
+    #     model(a)